Systems and methods for determination of blood flow characteristics and pathologies through modeling of myocardial blood supply

ABSTRACT

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving one or more vascular models associated with either the patient or with a plurality of individuals; receiving observed perfusion information associated with the patient; and estimating, using one or more computer processors, one or more blood flow characteristics or one or more pathological characteristics of the patient based on the observed perfusion information and the one or more vascular models.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/043,699 filed Aug. 29, 2014, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment, treatment planning, and related methods. Morespecifically, particular embodiments of the present disclosure relate tosystems and methods for determining blood flow characteristics andpathologies through modeling of myocardial blood supply.

BACKGROUND

Coronary artery disease is a common ailment that affects millions ofpeople. Coronary artery disease may cause the blood vessels providingblood to the heart to develop lesions, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack. Significant strides have been made in the treatment ofcoronary artery disease including both medical therapy (e.g. statins) orsurgical alternatives (e.g., percutaneous coronary intervention (PCI)and coronary artery bypass graft surgery (CABG)). Invasive assessmentsare commonly used to assess the type of treatment a patient may receive.However, indirect or noninvasive assessments for formulating a patienttreatment are being explored and developed.

One way of assessing the need for treatment, is observing blood supplyto tissue, since cardiovascular disease reduces blood supply to tissue(e.g., myocardial tissue). Several diagnostic modalities (e.g., computedtomography (CT angiography) and magnetic resonance imaging (MRangiography)) provide an assessment of blood supply to tissue (e.g.,perfusion, viability, or biomedical aspects of the tissue). While bloodsupply may provide some input for evaluating medical treatment,assessment of blood supply alone is inadequate for determining atargeted treatment. Heart disease is contingent on several more factors.

The inadequacy is made apparent by the fact that medical treatmentsprescribed are often more extreme than necessary. For example, PCI andbypass grafts are highly overused. In addition, PCI and bypass graftsare sometimes not used effectively, for instance, due to incorrectplacement or placement at stenoses that are not functionallysignificant. Thus, at a threshold level, a need exists to accuratelyassess the severity of cardiovascular disease in determining whichtreatment to apply to a patient. Furthermore, for PCI and bypass grafts,an accurate assessment of locations for applying treatment is important.Thus, a desire exists for improving treatment by targeting treatmentsites, both by accurately determining the severity of a disease and bypinpointing locations effective in treating the disease.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for determining blood flow characteristics andpathologies through modeling of myocardial blood supply.

One method includes: receiving one or more vascular models associatedwith either the patient or with a plurality of individuals; receivingobserved perfusion information associated with the patient; andestimating, using one or more computer processors, one or more bloodflow characteristics or one or more pathological characteristics of thepatient based on the observed perfusion information and the one or morevascular models.

In accordance with another embodiment, a system for evaluating a patientwith vascular disease comprises: a data storage device storinginstructions for evaluating a patient with vascular disease; and aprocessor configured for: receiving one or more vascular modelsassociated with either the patient or with a plurality of individuals;receiving observed perfusion information associated with the patient;and estimating, using one or more computer processors, one or more bloodflow characteristics or one or more pathological characteristics of thepatient based on the observed perfusion information and the one or morevascular models.

In accordance with another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofevaluating a patient with vascular disease, the method comprising:receiving one or more vascular models associated with either the patientor with a plurality of individuals; receiving observed perfusioninformation associated with the patient; and estimating, using one ormore computer processors, one or more blood flow characteristics or oneor more pathological characteristics of the patient based on theobserved perfusion information and the one or more vascular models.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network fordetermining blood flow characteristics and pathologies through modelingof myocardial blood supply, according to an exemplary embodiment of thepresent disclosure.

FIG. 2A is a block diagram of an exemplary method of determining therelationship between blood flow characteristics, pathologies, and tissuemodel variables, according to an exemplary embodiment of the presentdisclosure.

FIG. 2B is a block diagram of an exemplary method of estimating bloodflow characteristics and/or pathologies using a set of observed tissuevariables, according to an exemplary embodiment of the presentdisclosure.

FIG. 3A is a block diagram of an exemplary method of determining therelationship between blood flow characteristics, pathologies, and tissuevariables associated with one or more of perfusion deficit andmyocardial wall motion, according to an exemplary embodiment of thepresent disclosure.

FIG. 3B is a block diagram of an exemplary method of estimating bloodflow characteristics and pathologies using observed perfusion values,according to an exemplary embodiment of the present disclosure.

FIG. 3C is a block diagram of an exemplary method of estimating bloodflow characteristics and pathologies using observed wall motion values,according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Coronary artery disease is a common ailment, by which blood flow to theheart may be restricted. While significant strides have been made in thetreatment of coronary artery disease, the treatment is often misplacedor excessive. For example, patients often undergo invasive surgicaltreatments when medication may suffice. Patients are sometimes subjectedto treatments that may not change their condition. In some situations,patients even undergo treatments that ultimately worsen their condition.Thus, a need exists to accurately assess the severity of cardiovasculardisease in selecting a course of treatment. Additionally, a moreaccurate assessment of locations for applying treatments may providemore targeted treatment.

One way of assessing the need for treatment is by observing blood supplyto the patient's tissue, since cardiovascular disease typically involvesa reduction in blood supply to tissue (e.g., myocardial tissue). Severaldiagnostic modalities (e.g., computed tomography (CT angiography) andmagnetic resonance imaging (MR angiography)) provide an assessment ofblood supply to tissue (e.g., perfusion, viability, or biomedicalaspects of the tissue). However, assessment of blood supply, alone, isinadequate for determining appropriate treatment. For example, bloodsupply analysis, itself, cannot accurately localize a source ofcardiovascular disease and indicate a severity of a disease. Meanwhile,recent advances in imaging and computational modeling have provided ameans to virtually simulate blood flow for a patient, based on thecreation of a cardiovascular model for the patient.

The present disclosure includes systems and methods for improvingdisease and treatment assessment using metrics available from virtualsimulations, thus leveraging advances in biomechanical computationalmodeling with observational data about patient blood supply in order tolocalize and determine the severity of cardiovascular disease. In oneembodiment, the present disclosure includes working backward (e.g., in aso-called “inverse” fashion) from the observational blood supply data toinfer a likely computational model that describes the patient'scardiovascular disease. In other words, the disclosure includes findinga computational model that may explain the patient's observed bloodsupply data. In one embodiment, inferring the computational model mayinclude creating a cardiovascular model for the patient. The determinedcomputational model (and/or cardiovascular model) may provideinformation regarding severity and localization of cardiovasculardisease, that may form the basis for a patient treatment.

For example, the present disclosure may initially include determining arelationship between blood flow characteristics and/or pathologicalcharacteristics. The disclosure may further include determining howthose blood flow characteristics and/or pathological characteristicsrelate to blood supply to tissue. For example, a blood flowcharacteristic and/or a pathological characteristic including venousobstruction or low-flow may relate to low blood supply to tissue. Thepresent disclosure may further include observing variables associatedwith a patient's tissue, for instance, blood supply to the patient'stissue. Patient blood flow and patient pathological characteristics maybe estimated, given the known relationships between blood flowcharacteristics, pathological characteristics, and tissue blood supplyand the observed patient blood supply to tissue.

In some embodiments, patient blood flow and/or patient pathologicalcharacteristics may be computed from a cardiovascular model for thepatient. For example, one embodiment may include constructing acardiovascular model for the patient, using the observed blood supply tothe patient's tissue and the determined relationship between blood flowcharacteristics, pathological characteristics, and blood supply. Virtualsimulations of blood flow through the cardiovascular model, forinstance, may be used to compute the inferred patient blood flow and/orpatient pathological characteristics. The constructed patientcardiovascular model, estimated patient blood flow, and/or estimatedpatient pathological characteristics may reflect a patient conditioncontributing to the observed blood supply to patient tissue. The patientcondition may include characterizations of disease localization andseverity. Knowing disease severity and localizing the disease permitsformulation of appropriate, effective treatment. For the presentdisclosure, “patient” may refer to any individual of interest.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for determining blood flowcharacteristics and pathologies through modeling of myocardial bloodsupply, according to an exemplary embodiment. Specifically, FIG. 1depicts a plurality of physicians 102 and third party providers 104, anyof whom may be connected to an electronic network 101, such as theInternet, through one or more computers, servers, and/or handheld mobiledevices. Physicians 102 and/or third party providers 104 may create orotherwise obtain images of one or more patients' anatomy. The physicians102 and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, patient activity or exercise level, etc.Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIGS. 2A and 2B, in combination, provide steps for determining bloodflow characteristics and pathologies through modeling of myocardial orobserved blood supply, according to an exemplary embodiment. In oneembodiment, FIG. 2A may include steps to determine how characteristicsderived from a cardiovascular model may affect variables measured from atissue model. For example, the steps in FIG. 2A may include determiningthe relationship between a cardiovascular model's blood flow and/orpathological characteristics and a tissue model's associated perfusionvalues or myocardial wall motion values. In one embodiment, FIG. 2B mayinclude receiving or determining observed variable values measured frompatient tissue (e.g., blood supplied to the patient tissue) andestimating variables (e.g., vessel characteristics, including blood flowcharacteristics and/or pathological characteristics) using therelationship found from FIG. 2A. In other words, the method of FIG. 2Aincludes determining expected perfusion, given one or more anatomies andtissue models. FIG. 2B includes acquiring perfusion data, and thenperforming forward modeling with relationships found in FIG. 2A in orderto determine anatomy and tissue models (including blood flowcharacteristics and/or pathological characteristics) that may give riseto the acquired perfusion data. For instance, an anatomical model,alone, may not provide insight into an extent or impact of luminalnarrowing within various parts of the anatomical model. Forward modelingmay provide an expected perfusion map, which may be compared against,for instance, an acquired or observed patient perfusion map.

In a further embodiment, a user may input variations into assessmentsperformed in the method of FIG. 2A. The variations may provide insightinto agreement between actual and modeled perfusion data for the methodof FIG. 2B. For example, a user may select or input one or more types ofdisease (e.g., plaque severities) and/or one or more locations ofpotential plaque within an anatomical model. For the method of FIG. 2A,a user selection or input may prompt a showing of how thecharacteristics input by the user may influence perfusion. For themethod of FIG. 2B, a user selection or input may prompt a showing ofagreement between modeled perfusion data and observed (e.g.,patient-specific) perfusion data, given the user selection or input. Inother words, some embodiments of the methods of FIG. 2A and/or FIG. 2Bmay include providing users with options to test various anatomical,flow, and/or boundary conditions to see how these conditions may impactsimulated perfusion (e.g., in the form of simulated luminal narrowing).

For instance, the methods of FIGS. 2A and/or 2B may include renderingone or more user interfaces for receiving such user input, where theuser interface may also include a rendering of how the user's input mayimprove or decrease agreement between actual and modeled perfusion data.In an exemplary case, the renderings may include pictorial elements(e.g., representations of anatomy, perfusion or blood flow, plaque andplaque severities, etc.). In another exemplary case, the renderings mayinclude graphs, plots, or collections of data. The renderings mayfurther include color-coding, annotations, gradient shading, etc. Anunderstanding of factors that improve perfusion may provide the basisfor targeted treatment planning. In one embodiment, an extension of themethods of FIGS. 2A and 2B may include treatment analysis. For example,system 100 may further determine and/or output a treatment for apatient, based on the patient blood flow characteristics or patientpathological characteristics found through forward modeling using themethods of FIGS. 2A and 2B. The treatment may be determinedautomatically, for example, based on analyses to optimize perfusion inlight of the patient blood flow characteristics or patient pathologicalcharacteristics. Alternately or in addition, the output treatment may bebased on the user selections or inputs, for instance, simulating varioustreatments and/or treatment locations and observing how the simulationsimprove perfusion.

FIGS. 3A-3C may provide specific embodiments for the methods of FIGS. 2Aand 2B. For example, FIG. 3A may include an embodiment of the processfor finding a relationship between blood flow or pathologicalcharacteristics and tissue model characteristics. FIGS. 3B and 3C mayprovide specific embodiments pertaining to perfusion deficit andmyocardial wall motion, respectively. For example, FIG. 3B may include amethod using the relationship found in FIG. 3A to determine vesselpathologies that may explain perfusion values observed from a patient'smyocardium. Similarly, FIG. 3C may include a method using therelationship found in FIG. 3A to determine vessel pathologies that mayexplain wall motion values observed from a patient's myocardium.

FIG. 2A is a block diagram of an exemplary method 200 of determining therelationship between blood flow characteristics, pathologies, and tissuemodel variables, according to an exemplary embodiment. The method ofFIG. 2A may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101.

In one embodiment, step 201 includes receiving a cardiovascular model(e.g., in an electronic storage medium over electronic network 101). Inone embodiment, the cardiovascular model may include a cardiovascularmodel of an individual (e.g., a patient or individual of interest). Forexample, the cardiovascular model may be derived from images of theindividual, acquired via imaging or scanning modalities (e.g., CT scansor MR imaging). In one embodiment, step 203 may include receiving ordetermining blood flow characteristics and/or pathologicalcharacteristics in the cardiovascular model. Step 205 may includereceiving a tissue model (e.g., in an electronic storage medium). Step207 may include determining one or more variables associated with thetissue model (e.g., perfusion values or myocardial wall motion values).For example, step 207 may include defining or identifying one or morevariables that may be measured from the tissue model. For instance, theone or more variables may include variables that (directly orindirectly) measure blood supply to the tissue. Step 207 may furtherinclude calculating the values of the one or more defined or identifiedvariables.

In one embodiment, step 209 may include determining a relationshipbetween the one or more variables associated with the tissue model andthe blood flow characteristics and/or pathological characteristics inthe cardiovascular model. For example, step 209 may include determininga relationship between blood flow characteristics and pathologicalcharacteristics. In addition, step 209 may include determining an effectof blood flow characteristics and/or pathological characteristics on theone or more variables of the tissue model. In other words, step 209 mayinclude determining resultant tissue model variables, given a set ofblood flow and pathological conditions.

FIG. 2B is a block diagram of an exemplary schematic of a method 220 ofestimating blood flow characteristics and pathologies using a set ofobserved tissue variables, according to an exemplary embodiment. Themethod of FIG. 2B may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

In one embodiment, method 220 may include step 221 of actually measuringand/or observing one or more variables of a patient's tissue. Forexample, step 221 may include defining or identifying one or morevariables to observe in patient tissue (e.g., perfusion values ormyocardial wall motion values), and then monitoring measurements of theone or more variables via, e.g., imaging, in vitro diagnostics, visualinspection of the patient, etc. Step 223 may include associating the oneor more observed variables with a tissue model (e.g., the tissue modelof step 205). Step 223 may further include storing the one or moreobserved variables electronically (e.g., via an electronic storagemedium, RAM, etc.). In one embodiment, step 225 may include estimatingone or more blood flow characteristics and/or pathologicalcharacteristics of the patient's anatomy. For example, the estimatingperformed in step 225 may indicate that the patient's anatomy may berepresented by the cardiovascular model from step 201. In other words,step 225 may include estimating the patient blood flow and patientpathological characteristics in view of the one or more observedvariables associated with the patient. In some cases, step 225 may beperformed using a computing processor. In one embodiment, step 227 mayinclude outputting the estimates of the one or more patient blood flowcharacteristics and/or patient pathological characteristics (e.g., to anelectronic storage medium).

In some cases, a further step may include determining or recommending atreatment based on the estimates of the one or more patient blood flowcharacteristics and/or patient pathological characteristics. Forexample, determining or recommending a treatment may include receivingor creating one or more treatments associated with one or more bloodflow conditions or pathological conditions. Determining or recommendinga treatment may additionally or alternatively include identifyingtreatments associated with one or more combinations of blood flowcharacteristics and/or pathological characteristics. Method 220 mayfurther include finding or recommending treatment options that may beappropriate, given the estimated patient blood flow characteristicsand/or patient pathological characteristics.

FIG. 3A is a block diagram of an exemplary method 300 of determining therelationship between blood flow characteristics, pathologies, and tissuevariables associated with one or more of perfusion deficit andmyocardial wall motion, according to an exemplary embodiment. The methodof FIG. 3A may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 101.

Perfusion deficit analysis and myocardial wall motion analysis may beuseful for assessing the lack of blood supply to the myocardium of theheart. Perfusion deficit may be assessed with single photon emissioncomputed tomography (SPECT), positron emission tomography (PET),magnetic resonance (MR) perfusion, or coronary tomography (CT) perfusionimaging. Myocardial wall motion analysis may be assessed with ultrasoundor other forms of dynamic imaging (e.g., MR cine images, tagged MR, orfull phase CT imaging). Areas of the myocardium that do not receiveadequate blood supply may exhibit abnormal motion. Thus, observingmyocardial wall motion may be one way to evaluate perfusion.

However, perfusion deficit studies may not provide an assessment of thesource of the perfusion deficit. For example, a physician trying todetermine an effective patient treatment may not receive clear guidancefrom a perfusion deficit study alone. Similarly, wall motion studies(e.g., associated with myocardial wall motion) may not provide anassessment of the source of the wall motion abnormality and thereforealso may not provide clear guidance on a targeted treatment option.Consequently, perfusion deficit information and/or wall motioninformation may be used to determine that a blood flow problem exists,but perfusion deficit information and/or wall motion information, alone,may be insufficient in determining effective treatment.

Method 300 of FIG. 3A may assess the source of the perfusion deficit(e.g., in conjunction with method 320 of FIG. 3B) and/or the source ofthe wall motion abnormality (e.g., with method 340 of FIG. 3C).Knowledge of the source of the perfusion deficit and/or the wall motionabnormality may help determine an appropriate treatment for a givenpatient.

In one embodiment, step 301 may include receiving a coronary arterymodel (e.g., in an electronic storage medium). For example, the coronaryartery model may include a three-dimensional (3D) mesh model or aone-dimensional (1D) reduced order model. In some cases, the coronaryartery model may include a patient-specific model (e.g., a modelobtained via segmentation of a patient cardiac CT image). Alternately orin addition, the coronary artery model may include a generalized modelbased on population averages. Step 301 may include discerning coronarygeometry from imaging and further include discretizing the coronary treebased on potential locations of plaque and/or severity of potentialplaque. In one embodiment, step 303 may include receiving or identifyingvascular pathology or pathological state associated with the coronaryartery model.

In one embodiment, step 305 may include receiving a myocardium tissuemodel (e.g., in an electronic storage medium). For example, thepatient-specific myocardium tissue model may include a 3D volumetricmesh model or a two-dimensional (2D) surface mesh model. In some cases,the myocardium tissue model may include a patient-specific model (e.g.,obtained via segmentation of a patient cardiac CT image). Alternately orin addition, the myocardium tissue model may include a generalized modelbased on population averages. Certain blood flow characteristics and/orperfusion characteristics may be associated with the myocardium tissuemodel.

In one embodiment, step 307 may include selecting or evaluating arelationship between the coronary artery model and the myocardium tissuemodel. For example, step 307 may include finding the connection betweenvascular pathology and tissue, in terms of computational fluid dynamics.For instance, step 307 may include modeling perfusion using knownmethods (e.g., solving a reduced order model of blood flow in a networkof blood vessels generated to fill the tissue model, diffusion modeling,nearest-neighbor modeling).

In one embodiment, modeling perfusion in step 307 may include performingconstrained constructive optimization. One instance of constrainedconstructive optimization may include establishing one or more perfusionterritories, for example, within the coronary artery model, using themyocardium tissue model. The modeling of step 307 may further includeparameterizing one or more locations of plaque within the one or moreperfusion territories. Such parameterizing of location(s) of plaque maybe performed on either generic or patient-specific coronary arterymodels or myocardium tissue models. Provided with parameterizedlocation(s) of plaque, forward modeling may be used to model thequantity and rate of blood flow that may pass through the one or moreestablished perfusion territories. Modeling perfusion may relate thevascular pathologies to perfusion through the tissue model. In otherwords, step 307 may include recognizing correspondence between certainvascular pathologies and perfusion values found from the modeling.

In one embodiment, step 309 may include assessing the effect of vascularpathology on myocardial perfusion. For example, step 309 may includeanalyzing results of the modeling of step 307 to determine how fluiddynamics change with respect to vascular pathology. By extension, thechanges in fluid dynamics associated with vascular pathology may impactmyocardial perfusion. Step 309 may involve determining the influencethat vascular pathology may have on myocardial perfusion.

In one embodiment, step 309 may include analyzing effects of variousseverities of potential plaque within a coronary tree discretized intomultiple locations. In such an instance, step 309 may include receivingor prompting user input for severities of the potential plaque and/orlocations of possible plaque within the discretized coronary tree.Alternately, the selection of potential plaque severity and location maybe automated. In one instance, step 309 may include modeling perfusionthroughout one or more perfusion territories that encompass the selectedpossible locations and luminal narrowing associated with the possibleplaque severity. Alternately, the perfusion may be modeled throughselected portions of the one or more perfusion territories, where theselected portions of the one or more perfusion territories may includeone or more of the selected possible locations and luminal narrowingassociated with the possible plaque severity. The selected portions ofthe one or more perfusion territories may be selected automatically orby one or more users (e.g., upon prompting of user input).

FIG. 3B is a block diagram of an exemplary method 320 of estimatingblood flow characteristics and pathologies using observed perfusionvalues, according to an exemplary embodiment. The method of FIG. 3B maybe performed by server systems 106, based on information, images, anddata received from physicians 102 and/or third party providers 104 overelectronic network 101.

In one embodiment, step 321 may include observing perfusion values for apatient's myocardium (e.g., for the patient associated with the arterymodel and tissue model of steps 301 and 303). The perfusion values mayinclude measured or observed values for one or more perfusion variables.In one embodiment, perfusion values may be observed via SPECT, PET, MRperfusion, or CT perfusion imaging.

In one embodiment, step 323 may include associating the observedperfusion values with the myocardial perfusion values in the myocardiumtissue model (e.g., from step 303). For example, step 323 may includemaking the associations by registering the myocardium tissue model toimage(s) from which the perfusion values were observed (e.g., using acomputational processor). Step 323 may further include storing theobserved perfusion values electronically (e.g., via an electronicstorage medium, RAM, etc.).

In one embodiment, step 325 may include using a computing processor todetermine the set of vessel pathologies that may cause the observedperfusion values. For example, step 325 may include systematicallydeforming the coronary model to represent pathologies at one or moredifferent locations (e.g., by applying stenoses of different severitiesat given locations in the coronary model). For each of the deformations,step 325 may include applying computational fluid dynamics anddetermining a set of resulting myocardial perfusion values.

Furthermore, step 325 may include identifying one or more locations inthe coronary model, creating one or more hypothetical severities ofstenoses, and then applying these hypothetical stenos(es) at thelocation(s). Some or all of the hypothetical severities of stenoses maybe applied at each of the location(s) identified. In such cases, part ofstep 325 may include determining which of the one or more hypotheticalseverities of stenoses should be applied at which of the one or morelocations.

One form of step 325 may also include creating or selecting an estimateof the coronary model (e.g., a model of stenosis and computed bloodflow). For example, the created or selected coronary model may include acoronary model that agrees with the observed data (e.g., by selectingthe coronary model that results in a minimum of the maximum perfusiondifference between the observed and computed myocardium models). Forinstance, step 325 may include comparing various coronary models andidentifying or selecting a model that would most likely produce theobserved data. From the estimated coronary model, various blood flowcharacteristics including, fractional flow reserve, coronary flowreserve, etc., may be computed, including, fractional flow reserve,coronary flow reserve, etc. Another form of step 325 may include usingmachine learning methods to learn a set of vessel pathologies that maycorrespond to the observed variables.

In one embodiment, step 327 may include outputting the estimates of theone or more blood flow characteristics and/or pathologicalcharacteristics associated with the patient vessel pathologies (e.g.,based on the assessment of step 309). For example, step 327 may includeoutputting an estimate of one or more stenosis locations, stenosisseverities, and/or one or more fractional flow reserves to an electronicstorage medium. In one embodiment, the estimates of the one or moreblood flow characteristics and/or pathological characteristics may beoutput to an electronic storage medium. Step 327 may further includedetermining which of the estimates to output.

FIG. 3C is a block diagram of an exemplary method 340 of estimatingblood flow characteristics and pathologies using observed wall motionvalues, according to an exemplary embodiment. The method of FIG. 3C maybe performed by server systems 106, based on information, images, anddata received from physicians 102 and/or third party providers 104 overelectronic network 101.

In one embodiment, step 341 may include observing wall motion values forthe patient's myocardium (e.g., for the patient associated with theartery model and tissue model of steps 301 and 303). The wall motionvalues may include measured or observed values for one or more wallmotion variables. In one embodiment, wall motion values may be observedvia ultrasound, MR, or CT imaging.

In one embodiment, step 343 may include associating the observed wallmotion values with the wall motion values in the myocardium tissue model(e.g., from step 303). For example, step 343 may include making theassociations by registering the myocardium tissue model to image(s) fromwhich the perfusion values were observed using a computational processorat multiple frames. Wall motion values may be determined by computingdisplacement of the myocardial wall between frames. Step 343 may furtherinclude storing the wall motion variables electronically (e.g., via anelectronic storage medium, RAM, etc.).

In one embodiment, step 345 may include using a computing processor todetermine the set of vessel pathologies that would result in theobserved wall motion values. For example, step 345 may includesystematically deforming the coronary model to represent pathologies atdifferent locations (e.g., by applying stenoses of different severitiesat equally spaced locations in the coronary model). For each of thedeformations, step 345 may include applying computational fluid dynamicsand determining a set of resulting myocardial perfusion values. Step 345may further include mapping these perfusion values to wall motionvalues.

In addition, step 345 may include identifying one or more locations inthe coronary model, creating one or more hypothetical severities ofstenoses, then applying these hypothetical severities of stenos(es) atthe location(s). Some or all of the hypothetical severities of stenosesmay be applied at each of the location(s) identified. In such cases,part of step 345 may include determining which of the one or morelocations to apply to which of the one or more hypothetical severitiesof stenoses.

One form of step 345 may include creating or selecting an estimate ofthe coronary model (stenosis and computed blood flow) as the coronarymodel (stenosis and computed blood flow). For example, the created orselected coronary model may include a model that reflects the observeddata (e.g., by selecting the coronary model that results in a minimum ofthe maximum wall motion difference between the observed and computedmyocardium models). For instance, step 345 may include comparing variouscoronary models and selecting a model from the comparison that wouldmost likely produce the observed data. The estimated coronary model maybe used to compute blood flow characteristics, including fractional flowreserve, coronary flow reserve, etc. Another form of step 345 mayinclude using machine learning methods to learn a set of vesselpathologies that may correspond to the observed variables.

In one embodiment, step 347 may include outputting the estimates of theone or more blood flow characteristics and/or pathologicalcharacteristics associated with the vessel pathologies (e.g., based onthe assessment of step 309). For example, step 347 may includeoutputting an estimate of one or more stenosis locations, stenosisseverities, and one or more fractional flow reserve values to anelectronic storage medium. In one embodiment, the estimates of the oneor more blood flow characteristics and/or pathological characteristicsmay be output to an electronic storage medium. Step 347 may furtherinclude determining which of the estimates to output.

The present disclosure may further apply to organs and tissues otherthan the heart. For example, the present disclosure may apply to anyscenarios where it is desirable to relate a measurement of the functionof the tissue or organ to a consideration of the blood supply throughthe arterial network supplying that tissue or organ. One such scenariomay involve relating perfusion measured during a functional brainimaging study to the vessels supplying the brain, in order to identifythe location of disease affecting cerebral blood supply. Thisunderstanding of cerebral blood supply may be important for patientsthat have had a stroke or a transient ischemia attack (TIA).

Alternatively or additionally, the present disclosure may be useful inrelating disease in vessels supplying a patient's legs, to regions ofreduced flow in the muscles of the lower extremities. In one embodiment,the reduced flow may be observed (e.g., imaged) during or after physicalactivity. For example, perfusion imaging may be performed using magneticresonance imaging methods while the patient is exercising on anMR-compatible ergometer. Severity or location of disease in vessels maybe determined from observed exercise perfusion values. Treatments maythen be formulated based on this understanding of the disease.

In an alternative embodiment, the present disclosure may be used toquantify the significance of renal artery disease. For example, theembodiment may include identifying relationships between a vascularmodel (e.g., generated using magnetic resonance angiography data) and atissue model (e.g., generated using kidney oxygenation measurementsobtained with BOLD (Blood Oxygen-Level-Dependent) magnetic resonanceimaging). Given an individual's kidney oxygenation measurements, apatient vascular model may be built using the known relationships. Thesignificance of the patient's renal artery disease may be found from thepatient vascular model.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method of generating apatient-specific perfusion treatment recommendation, the methodcomprising: receiving a vascular model associated with one or moreindividuals; receiving a tissue model associated with the one or moreindividuals; generating an expected perfusion map using the receivedvascular model and the received tissue model; receiving, for a givenpatient, a patient-specific observed perfusion value; and generating atreatment recommendation based on the expected perfusion map and thepatient-specific observed perfusion value.
 22. The computer-implementedmethod of claim 21, wherein the expected perfusion map includes avascular pathology corresponding to a perfusion value.
 23. Thecomputer-implemented method of claim 21, further comprising: calculatinga first perfusion value using the vascular model and the tissue model;determining, using a fluid dynamics computation, a first vascularpathology of the vascular model used for the calculation of the firstperfusion value; and generating the expected perfusion map from thefirst vascular pathology and the calculated first perfusion value. 24.The computer-implemented method of claim 21, further comprising:deforming a stored vascular model based on the expected perfusion mapand the received patient-specific observed perfusion value; generating apatient-specific vascular model based on the deformed vascular model;and generating the treatment recommendation based on the generatedpatient-specific vascular model.
 25. The computer-implemented method ofclaim 24, further comprising: calculating a second perfusion value usingthe deformed vascular model; and generating the patient-specificvascular model as a vascular model for which the second perfusion valuematches the patient-specific observed perfusion value.
 26. Thecomputer-implemented method of claim 24, further comprising: estimatingone or more patient pathological characteristics based on the generatedpatient-specific vascular model.
 27. The computer-implemented method ofclaim 26, further comprising: identifying a set of hypothetical vesselpathologies that produce the observed tissue characteristic value,wherein the one or more patient pathological characteristics areassociated with the set of vessel pathologies.
 28. Thecomputer-implemented method of claim 24, further comprising: receiving aperfusion value associated with the received tissue model; andregistering the received perfusion value to a location of thepatient-specific vascular model; and generating the treatmentrecommendation based on the registration of the received perfusion valueto the location of the vascular model.
 29. A system for generating apatient-specific perfusion treatment recommendation, the systemcomprising: a data storage device storing instructions for generating apatient-specific perfusion treatment recommendation; and a processorconfigured to execute the instructions to perform a method including:receiving a vascular model associated with one or more individuals;receiving a tissue model associated with the one or more individuals;generating an expected perfusion map using the received vascular modeland the received tissue model; receiving, for a given patient, apatient-specific observed perfusion value; and generating a treatmentrecommendation based on the expected perfusion map and thepatient-specific observed perfusion value.
 30. The system of claim 29,wherein the expected perfusion map includes a vascular pathologycorresponding to a perfusion value.
 31. The system of claim 29, whereinthe system is further configured for: calculating a first perfusionvalue using the vascular model and the tissue model; determining, usinga fluid dynamics computation, a first vascular pathology of the vascularmodel used for the calculation of the first perfusion value; andgenerating the expected perfusion map from the first vascular pathologyand the calculated first perfusion value.
 32. The system of claim 29,wherein the system is further configured for: deforming a storedvascular model based on the expected perfusion map and the receivedpatient-specific observed perfusion value; generating a patient-specificvascular model based on the deformed vascular model; and generating thetreatment recommendation based on the generated patient-specificvascular model.
 33. The system of claim 32, wherein the system isfurther configured for: calculating a second perfusion value using thedeformed vascular model; and generating the patient-specific vascularmodel as a vascular model for which the second perfusion value matchesthe patient-specific observed perfusion value.
 34. The system of claim32, wherein the system is further configured for: estimating one or morepatient pathological characteristics based on the generatedpatient-specific vascular model.
 35. The system of claim 34, wherein thesystem if further configured for: identifying a set of hypotheticalvessel pathologies that produce the observed tissue characteristicvalue, wherein the one or more patient pathological characteristics areassociated with the set of vessel pathologies.
 36. The system of claim32, wherein the system is further configured for: receiving a perfusionvalue associated with the received tissue model; and registering thereceived perfusion value to a location of the patient-specific vascularmodel; and generating the treatment recommendation based on theregistration of the received perfusion value to the location of thevascular model.
 37. A non-transitory computer readable medium for use ona computer system containing computer-executable programminginstructions for generating a patient-specific perfusion treatmentrecommendation, the method comprising: receiving a vascular modelassociated with one or more individuals; receiving a tissue modelassociated with the one or more individuals; generating an expectedperfusion map using the received vascular model and the received tissuemodel; receiving, for a given patient, a patient-specific observedperfusion value; and generating a treatment recommendation based on theexpected perfusion map and the patient-specific observed perfusionvalue.
 38. The non-transitory computer readable medium of claim 37,wherein the expected perfusion map includes a vascular pathologycorresponding to a perfusion value.
 39. The non-transitory computerreadable medium of claim 37, the method further comprising: calculatinga first perfusion value using the vascular model and the tissue model;determining, using a fluid dynamics computation, a first vascularpathology of the vascular model used for the calculation of the firstperfusion value; and generating the expected perfusion map from thefirst vascular pathology and the calculated first perfusion value. 40.The non-transitory computer readable medium of claim 37, the methodfurther comprising: deforming a stored vascular model based on theexpected perfusion map and the received patient-specific observedperfusion value; generating a patient-specific vascular model based onthe deformed vascular model; and generating the treatment recommendationbased on the generated patient-specific vascular model.